I) Préparation de l’environnement Python et importation des données

1) Importation des libraries

import pandas as pd
import plotly.express as px
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from plotnine import *

2) Importation des données

aide_alimentaire = pd.read_csv("DATA_en/aide_alimentaire_en.csv", sep=";")
sous_nutrition_en = pd.read_csv("DATA_en/sous_nutrition_en.csv", sep=";")
bilan_cereale = pd.read_csv("DATA_en/bilan_cereale.csv", sep=";")
dispo_alimentaire_vegetal_en = pd.read_csv("DATA_en/dispo_alimentaire_vegetal_en.csv", sep=";")
dispo_alimentaire_animal_en = pd.read_csv("DATA_en/dispo_alimentaire_animal_en.csv", sep=";")
population_en = pd.read_csv("DATA_en/population_en.csv", sep=";")
pib_en = pd.read_csv("DATA_en/pib_en.csv", sep=";")

print(aide_alimentaire.head(3))
print("____________________________________")
print(sous_nutrition_en.head(3))
print("____________________________________")
print(bilan_cereale.head(3))
print("____________________________________")
print(dispo_alimentaire_vegetal_en.head(3))
print("____________________________________")
print(dispo_alimentaire_animal_en.head(3))
print("____________________________________")
print(population_en.head(3))
print("____________________________________")
print(pib_en.head(3))

   Recipient Country Code (FAO) Recipient Country  Element Code  \
0                             2       Afghanistan           500   
1                             2       Afghanistan           500   
2                             2       Afghanistan           500   

             Element  Item Code                Item  Year    Unit  Value  
0  Food aid received      12061             Cereals  2013  tonnes  40504  
1  Food aid received      12061             Cereals  2014  tonnes  15989  
2  Food aid received      11733  Dried Fruits Total  2013  tonnes     85  
____________________________________
   AreaCode         Area  ElementCode Element  ItemCode  \
0         2  Afghanistan         6121   Value    210041   
1         2  Afghanistan         6132   Value    210011   
2         3      Albania         6121   Value    210041   

                                                Item       Year      Unit  \
0  Prevalence of undernourishment (percent) (3-ye...  2016-2018         %   
1  Number of people undernourished (million) (3-y...  2016-2018  millions   
2  Prevalence of undernourishment (percent) (3-ye...  2016-2018         %   

  Value  Note  
0    23   NaN  
1   8.3   NaN  
2   4.1   NaN  
____________________________________
   Area Code         Area  Element Code                Element  Year  \
0          2  Afghanistan          5521  Aliments.pour.animaux  2017   
1          2  Afghanistan          5527               Semences  2017   
2          2  Afghanistan          5123                 Pertes  2017   

          Unit  Value  
0  1000 tonnes    403  
1  1000 tonnes    387  
2  1000 tonnes    706  
____________________________________
    Domain  AreaCode         Area  ElementCode          Element  ItemCode  \
0  Vegetal         2  Afghanistan         5511       Production      2511   
1  Vegetal         2  Afghanistan         5611  Import Quantity      2511   
2  Vegetal         2  Afghanistan         5072  Stock Variation      2511   

                 Item  Year         Unit   Value  
0  Wheat and products  2017  1000 tonnes  4281.0  
1  Wheat and products  2017  1000 tonnes  3080.0  
2  Wheat and products  2017  1000 tonnes   208.0  
____________________________________
   Domain  AreaCode         Area  ElementCode          Element  ItemCode  \
0  Animal         2  Afghanistan         5511       Production      2731   
1  Animal         2  Afghanistan         5611  Import Quantity      2731   
2  Animal         2  Afghanistan         5072  Stock Variation      2731   

          Item  Year         Unit  Value  
0  Bovine Meat  2017  1000 tonnes   95.0  
1  Bovine Meat  2017  1000 tonnes    7.0  
2  Bovine Meat  2017  1000 tonnes   -1.0  
____________________________________
   Area Code         Area  Element Code                        Element  \
0          2  Afghanistan           511  Total Population - Both sexes   
1          3      Albania           511  Total Population - Both sexes   
2          4      Algeria           511  Total Population - Both sexes   

   Item Code        Item  Year          Unit  Value  
0       2501  Population  2017  1000 persons  36296  
1       2501  Population  2017  1000 persons   2884  
2       2501  Population  2017  1000 persons  41389  
____________________________________
   AreaCode         Area  ElementCode               Element  Item Code  \
0         2  Afghanistan         6110             Value US$      22008   
1         2  Afghanistan         6119  Value US$ per capita      22008   
2         3      Albania         6110             Value US$      22008   

                     Item  Year      Unit         Value  
0  Gross Domestic Product  2017  millions  18623.026651  
1  Gross Domestic Product  2017       US$    513.085978  
2  Gross Domestic Product  2017  millions  13019.729856  

#concaténation du DF Dispo_alimentaire_vegetal_en avec le DF de la dispo alimentaire d'origine animale
dispo_alimentaire_total = pd.concat([dispo_alimentaire_vegetal_en, dispo_alimentaire_animal_en]) 
dispo_alimentaire_total

	Domain	AreaCode	Area	ElementCode	Element	ItemCode	Item	Year	Unit	Value
0	Vegetal	2	Afghanistan	5511	Production	2511	Wheat and products	2017	1000 tonnes	4281.0
1	Vegetal	2	Afghanistan	5611	Import Quantity	2511	Wheat and products	2017	1000 tonnes	3080.0
2	Vegetal	2	Afghanistan	5072	Stock Variation	2511	Wheat and products	2017	1000 tonnes	208.0
3	Vegetal	2	Afghanistan	5911	Export Quantity	2511	Wheat and products	2017	1000 tonnes	2.0
4	Vegetal	2	Afghanistan	5301	Domestic supply quantity	2511	Wheat and products	2017	1000 tonnes	7151.0
...	...	...	...	...	...	...	...	...	...	...
41780	Animal	181	Zimbabwe	5142	Food	2775	Aquatic Plants	2017	1000 tonnes	0.0
41781	Animal	181	Zimbabwe	645	Food supply quantity (kg/capita/yr)	2775	Aquatic Plants	2017	kg	0.0
41782	Animal	181	Zimbabwe	664	Food supply (kcal/capita/day)	2775	Aquatic Plants	2017	kcal/capita/day	0.0
41783	Animal	181	Zimbabwe	674	Protein supply quantity (g/capita/day)	2775	Aquatic Plants	2017	g/capita/day	0.0
41784	Animal	181	Zimbabwe	684	Fat supply quantity (g/capita/day)	2775	Aquatic Plants	2017	g/capita/day	0.0

183688 rows × 10 columns

II) Analyse de la sous-nutrition mondiale

1) Proportion de personnes en état de sous-nutrition en 2017

# Somme de la population mondiale
Total_pop = population_en['Value'].sum()*1000
print(Total_pop)

7481124000

# Filtre des données : On en garde que le nombre de personne en état de sous nutrition (ElementCode 6132) 
# et les pays où ce nombre est différent de "<0.1" et de "NaN"
ssNutri_filter = sous_nutrition_en.loc[(sous_nutrition_en['ElementCode'] == 6132) & (sous_nutrition_en['Value'] != '<0.1')].dropna(subset = ['Value'])

ssNutri_filter.dtypes

AreaCode         int64
Area            object
ElementCode      int64
Element         object
ItemCode         int64
Item            object
Year            object
Unit            object
Value           object
Note           float64
dtype: object

# Transformation du types de 'Value' en numérique décimal (float) 
ssNutri_filter['Value']=pd.to_numeric(ssNutri_filter.Value)
ssNutri_filter.dtypes

AreaCode         int64
Area            object
ElementCode      int64
Element         object
ItemCode         int64
Item            object
Year            object
Unit            object
Value          float64
Note           float64
dtype: object

# Calcul du nombre total de personne en état de sous-nutrition dans le monde

total_ssNutri=ssNutri_filter['Value'].sum()*1000000
print(total_ssNutri)

554800000.0

# Calcul du pourcentage de personne en état de sous-nutrition dans le monde
round((total_ssNutri/Total_pop)*100,2)

7.42

Représentation du nombre de personne en sous-nutrition par pays

fig = px.bar(ssNutri_filter, x='Area', y='Value',color='Value',
                         labels={
                     "Value": "Nombre d'habitants<br>en sous-nutrition (en million)",
                     "Area": "Pays"})
fig.update_layout(xaxis={'categoryorder':'total descending'})
fig.update_coloraxes(colorscale='Viridis')
#fig.update_traces(marker_coloraxis=None)
fig.update_layout(xaxis={"dtick":1},margin={"t":0,"b":0},height=500)
fig.update_xaxes(tickfont=dict(size=8))
fig.show()

Représentation du pourcentage de personne en sous-nutrition par pays

ssNutri_Prev_filter = sous_nutrition_en.loc[(sous_nutrition_en['ElementCode'] == 6121) & (sous_nutrition_en['Value'] != '<2.5')].dropna(subset = ['Value'])
ssNutri_Prev_filter.dtypes

AreaCode         int64
Area            object
ElementCode      int64
Element         object
ItemCode         int64
Item            object
Year            object
Unit            object
Value           object
Note           float64
dtype: object

ssNutri_Prev_filter['Value']=pd.to_numeric(ssNutri_Prev_filter.Value)
ssNutri_Prev_filter.dtypes

AreaCode         int64
Area            object
ElementCode      int64
Element         object
ItemCode         int64
Item            object
Year            object
Unit            object
Value          float64
Note           float64
dtype: object

fig = px.bar(ssNutri_Prev_filter, x='Area', y='Value', color ='Value',
             color_continuous_scale=px.colors.sequential.Viridis,
             labels={"Value": "Pourcentage de la population<br>en sous-nutrition",
                     "Area": "Pays"})
#fig.update_traces(marker_coloraxis=None)
fig.update_layout(xaxis={'categoryorder':'total descending'})
fig.update_layout(xaxis={"dtick":1},margin={"t":0,"b":0},height=500)
fig.update_xaxes(tickfont=dict(size=10))
fig.show()

2) Répartition de l’aide alimentaire entre 2013 à 2016

aide_alimentaire.head()

	Recipient Country Code (FAO)	Recipient Country	Element Code	Element	Item Code	Item	Year	Unit	Value
0	2	Afghanistan	500	Food aid received	12061	Cereals	2013	tonnes	40504
1	2	Afghanistan	500	Food aid received	12061	Cereals	2014	tonnes	15989
2	2	Afghanistan	500	Food aid received	11733	Dried Fruits Total	2013	tonnes	85
3	2	Afghanistan	500	Food aid received	12062	Non-Cereals	2013	tonnes	23615
4	2	Afghanistan	500	Food aid received	12062	Non-Cereals	2014	tonnes	12618

# Faire la somme de l'aide alimentaire : aggrégation par Recipient Country
Aide_aliSUm = aide_alimentaire.groupby(['Recipient Country','Year'], as_index=False).sum()

Aide_aliSUm.head()

	Recipient Country	Year	Recipient Country Code (FAO)	Element Code	Item Code	Value
0	Afghanistan	2013	16	4000	93050	128238
1	Afghanistan	2014	16	4000	93207	57214
2	Algeria	2013	44	5500	127026	35234
3	Algeria	2014	40	5000	115293	18980
4	Algeria	2015	40	5000	115238	17424

Aide_aliSUm = Aide_aliSUm.sort_values('Value',ascending=False)

fig = px.bar(Aide_aliSUm, x='Recipient Country', y='Value', facet_row='Year', color='Value',
             color_continuous_scale=px.colors.sequential.Viridis,
            labels={
                     "Value": "Aide alimentaire<br>reçue (tonnes)",
                     "Recipient Country": "Pays bénéficiaire",
                     "Year": "Année",
                 })
#fig.update_traces(marker_coloraxis=None)
fig.update_layout(xaxis={"dtick":1},margin={"t":0,"b":0},height=800)
fig.update_xaxes(tickfont=dict(size=8))
fig.update(layout_showlegend=False)
fig.update_layout({
    'legend_title_text': ''})
fig.show()

3) Résumé des 10 pays les plus en difficulté alimentaire

En terme de pourcentage de population :

ssNutri_Prev_10 = ssNutri_Prev_filter.nlargest(10, 'Value')
print(ssNutri_Prev_10)

     AreaCode                                   Area  ElementCode Element  \
336       201                                Somalia         6121   Value   
162        93                                  Haiti         6121   Value   
400       249                                  Yemen         6121   Value   
68         37               Central African Republic         6121   Value   
102       116  Democratic People's Republic of Korea         6121   Value   
216       129                             Madagascar         6121   Value   
104       250       Democratic Republic of the Congo         6121   Value   
176       103                                   Iraq         6121   Value   
208       123                                Liberia         6121   Value   
86         46                                  Congo         6121   Value   

     ItemCode                                               Item       Year  \
336    210041  Prevalence of undernourishment (percent) (3-ye...  2016-2018   
162    210041  Prevalence of undernourishment (percent) (3-ye...  2016-2018   
400    210041  Prevalence of undernourishment (percent) (3-ye...  2016-2018   
68     210041  Prevalence of undernourishment (percent) (3-ye...  2016-2018   
102    210041  Prevalence of undernourishment (percent) (3-ye...  2016-2018   
216    210041  Prevalence of undernourishment (percent) (3-ye...  2016-2018   
104    210041  Prevalence of undernourishment (percent) (3-ye...  2016-2018   
176    210041  Prevalence of undernourishment (percent) (3-ye...  2016-2018   
208    210041  Prevalence of undernourishment (percent) (3-ye...  2016-2018   
86     210041  Prevalence of undernourishment (percent) (3-ye...  2016-2018   

    Unit  Value  Note  
336    %   58.7   NaN  
162    %   48.0   NaN  
400    %   46.6   NaN  
68     %   46.1   NaN  
102    %   42.9   NaN  
216    %   42.3   NaN  
104    %   40.2   NaN  
176    %   37.9   NaN  
208    %   36.6   NaN  
86     %   36.0   NaN  

fig = px.bar(ssNutri_Prev_10, x='Area', y='Value', color ='Value',
             color_continuous_scale=px.colors.sequential.Viridis,
             labels={
                    "Value": "Pourcentage de la population<br>en sous-nutrition",
                     "Area": "Pays" })
#fig.update_traces(marker_coloraxis=None)
fig.update_layout(xaxis={'categoryorder':'total descending'})
fig.update_layout(xaxis={"dtick":1},margin={"t":0,"b":0},height=500)
fig.update_xaxes(tickfont=dict(size=12))
fig.show()

## Figure à la verticale ##
#fig = px.bar(ssNutri_Prev_10, y='Area', x='Value', color ='Value',
#             color_continuous_scale=px.colors.sequential.Viridis,
#             labels={
#                    "Value": "Pourcentage de la population<br>en sous-nutrition",
#                     "Area": "Pays" })
#fig.update_layout(yaxis={'categoryorder':'total ascending'},
#                 height=1000,
#                 width=800)
#fig.update_layout(xaxis={"dtick":1},margin={"t":0,"b":0},height=500)
#fig.update_xaxes(tickfont=dict(size=15))
#fig.show()

En terme de nombre de personnes :

ssNutri_Tot_10 = ssNutri_filter.nlargest(10, 'Value')
print(ssNutri_Tot_10)

     AreaCode                              Area  ElementCode Element  \
171       100                             India         6132   Value   
105       250  Democratic Republic of the Congo         6132   Value   
275       165                          Pakistan         6132   Value   
29         16                        Bangladesh         6132   Value   
265       159                           Nigeria         6132   Value   
173       101                         Indonesia         6132   Value   
129       238                          Ethiopia         6132   Value   
177       103                              Iraq         6132   Value   
387       215       United Republic of Tanzania         6132   Value   
401       249                             Yemen         6132   Value   

     ItemCode                                               Item       Year  \
171    210011  Number of people undernourished (million) (3-y...  2016-2018   
105    210011  Number of people undernourished (million) (3-y...  2016-2018   
275    210011  Number of people undernourished (million) (3-y...  2016-2018   
29     210011  Number of people undernourished (million) (3-y...  2016-2018   
265    210011  Number of people undernourished (million) (3-y...  2016-2018   
173    210011  Number of people undernourished (million) (3-y...  2016-2018   
129    210011  Number of people undernourished (million) (3-y...  2016-2018   
177    210011  Number of people undernourished (million) (3-y...  2016-2018   
387    210011  Number of people undernourished (million) (3-y...  2016-2018   
401    210011  Number of people undernourished (million) (3-y...  2016-2018   

         Unit  Value  Note  
171  millions  184.9   NaN  
105  millions   32.7   NaN  
275  millions   25.2   NaN  
29   millions   20.0   NaN  
265  millions   19.2   NaN  
173  millions   16.8   NaN  
129  millions   14.9   NaN  
177  millions   14.2   NaN  
387  millions   13.2   NaN  
401  millions   13.0   NaN  

fig = px.bar(ssNutri_Tot_10, x='Area', y='Value', color ='Value',
            color_continuous_scale=px.colors.sequential.Viridis,
             labels={
                     "Value": "Nombre d'habitants<br>en sous-nutrition (en million)",
                     "Area": "Pays"})
#fig.update_traces(marker_coloraxis=None)
fig.update_layout(xaxis={'categoryorder':'total descending'})
fig.update_layout(xaxis={"dtick":1},margin={"t":0,"b":0},height=500)
fig.update_xaxes(tickfont=dict(size=12))
fig.show()

Au niveau de l'aide alimentaire :

#Liste des 10 pays ayant reçu le plus d'aide alimentaire depuis 2013
Aide_aliSUmTot= aide_alimentaire.groupby(['Recipient Country'], as_index=False).sum()
Aide_aliSUmTot10 = Aide_aliSUmTot.nlargest(10, 'Value')
print(Aide_aliSUmTot10)

                   Recipient Country  Recipient Country Code (FAO)  \
66              Syrian Arab Republic                          8056   
27                          Ethiopia                          7854   
73                             Yemen                          9462   
63                       South Sudan                          6094   
65                             Sudan                          6624   
40                             Kenya                          3078   
3                         Bangladesh                           400   
62                           Somalia                          5427   
20  Democratic Republic of the Congo                          6250   
55                             Niger                          4898   

    Element Code  Item Code   Year    Value  
66         19000     436980  76543  1858943  
27         16500     379389  66460  1381294  
73         19000     438644  76553  1206484  
63         11000     257907  44309   695248  
65         12000     280270  48335   669784  
40         13500     315569  54376   552836  
3          12500     288220  50347   348188  
62         13500     313635  54377   292678  
20         12500     288156  50350   288502  
55         15500     347987  62448   276344  

fig = px.bar(Aide_aliSUmTot10, x='Recipient Country', y='Value', color ='Value',
                          color_continuous_scale=px.colors.sequential.Viridis,
            labels={
                     "Value": "Aide alimentaire<br>reçue (tonnes)",
                     "Recipient Country": "Pays bénéficiaire",
                     "Year": "Année",
                 })
#fig.update_traces(marker_coloraxis=None)
fig.update_layout(xaxis={'categoryorder':'total descending'})
fig.update_layout(xaxis={"dtick":1},margin={"t":0,"b":0},height=500)
fig.update_xaxes(tickfont=dict(size=12))
fig.show()

III) Analyse de la disponibilité alimentaire en 2017

1) Le nombre théorique de personnes qui pourraient être nourries en 2017

Pour calculer le nombre théorique de personne qui pourraient être nourries en 2017, il faut tout d’abord additionner la disponibilité energétique en Kcal/personne/jour) de chaque catégorie d’aliments par pays.

dispo_alimentaire_total

	Domain	AreaCode	Area	ElementCode	Element	ItemCode	Item	Year	Unit	Value
0	Vegetal	2	Afghanistan	5511	Production	2511	Wheat and products	2017	1000 tonnes	4281.0
1	Vegetal	2	Afghanistan	5611	Import Quantity	2511	Wheat and products	2017	1000 tonnes	3080.0
2	Vegetal	2	Afghanistan	5072	Stock Variation	2511	Wheat and products	2017	1000 tonnes	208.0
3	Vegetal	2	Afghanistan	5911	Export Quantity	2511	Wheat and products	2017	1000 tonnes	2.0
4	Vegetal	2	Afghanistan	5301	Domestic supply quantity	2511	Wheat and products	2017	1000 tonnes	7151.0
...	...	...	...	...	...	...	...	...	...	...
41780	Animal	181	Zimbabwe	5142	Food	2775	Aquatic Plants	2017	1000 tonnes	0.0
41781	Animal	181	Zimbabwe	645	Food supply quantity (kg/capita/yr)	2775	Aquatic Plants	2017	kg	0.0
41782	Animal	181	Zimbabwe	664	Food supply (kcal/capita/day)	2775	Aquatic Plants	2017	kcal/capita/day	0.0
41783	Animal	181	Zimbabwe	674	Protein supply quantity (g/capita/day)	2775	Aquatic Plants	2017	g/capita/day	0.0
41784	Animal	181	Zimbabwe	684	Fat supply quantity (g/capita/day)	2775	Aquatic Plants	2017	g/capita/day	0.0

183688 rows × 10 columns

# On filtre le jeu de donnée pour récupérer uniquement Food supply (kcal/capita/day), corerspondant a l'ElementCode 664
FoodSupply_filter = dispo_alimentaire_total.loc[(dispo_alimentaire_total['ElementCode'] == 664)].dropna(subset = ['Value'])
FoodSupply_filter

	Domain	AreaCode	Area	ElementCode	Element	ItemCode	Item	Year	Unit	Value
11	Vegetal	2	Afghanistan	664	Food supply (kcal/capita/day)	2511	Wheat and products	2017	kcal/capita/day	1404.0
25	Vegetal	2	Afghanistan	664	Food supply (kcal/capita/day)	2805	Rice (Milled Equivalent)	2017	kcal/capita/day	181.0
39	Vegetal	2	Afghanistan	664	Food supply (kcal/capita/day)	2513	Barley and products	2017	kcal/capita/day	14.0
54	Vegetal	2	Afghanistan	664	Food supply (kcal/capita/day)	2514	Maize and products	2017	kcal/capita/day	9.0
64	Vegetal	2	Afghanistan	664	Food supply (kcal/capita/day)	2515	Rye and products	2017	kcal/capita/day	0.0
...	...	...	...	...	...	...	...	...	...	...
41749	Animal	181	Zimbabwe	664	Food supply (kcal/capita/day)	2765	Crustaceans	2017	kcal/capita/day	0.0
41757	Animal	181	Zimbabwe	664	Food supply (kcal/capita/day)	2766	Cephalopods	2017	kcal/capita/day	0.0
41766	Animal	181	Zimbabwe	664	Food supply (kcal/capita/day)	2767	Molluscs, Other	2017	kcal/capita/day	0.0
41774	Animal	181	Zimbabwe	664	Food supply (kcal/capita/day)	2769	Aquatic Animals, Others	2017	kcal/capita/day	0.0
41782	Animal	181	Zimbabwe	664	Food supply (kcal/capita/day)	2775	Aquatic Plants	2017	kcal/capita/day	0.0

15150 rows × 10 columns

# Calcul de la somme par Pays

Foodsupply_sum=FoodSupply_filter.groupby(['Area'], as_index=False).sum()
Foodsupply_sum

	Area	AreaCode	ElementCode	ItemCode	Year	Value
0	Afghanistan	152	50464	199095	153292	2303.0
1	Albania	234	51792	206162	157326	3324.0
2	Algeria	336	55776	221444	169428	3380.0
3	Angola	602	57104	226503	173462	2436.0
4	Antigua and Barbuda	696	57768	229025	175479	2444.0
...	...	...	...	...	...	...
173	Venezuela (Bolivarian Republic of)	20768	58432	231853	177496	2353.0
174	Viet Nam	20382	57104	226402	173462	2895.0
175	Yemen	21912	58432	231568	177496	1991.0
176	Zambia	22590	59760	236854	181530	2234.0
177	Zimbabwe	16290	59760	236949	181530	1860.0

178 rows × 6 columns

#Création d'une colonne SupplyPerCapita, correspondant à la part des besoins eneregétiques pouvant être comblé par la production du pays per capita.  
Foodsupply_sum['SupplyPerCapita']=Foodsupply_sum['Value']/2400

Foodsupply_sum

	Area	AreaCode	ElementCode	ItemCode	Year	Value	SupplyPerCapita
0	Afghanistan	152	50464	199095	153292	2303.0	0.959583
1	Albania	234	51792	206162	157326	3324.0	1.385000
2	Algeria	336	55776	221444	169428	3380.0	1.408333
3	Angola	602	57104	226503	173462	2436.0	1.015000
4	Antigua and Barbuda	696	57768	229025	175479	2444.0	1.018333
...	...	...	...	...	...	...	...
173	Venezuela (Bolivarian Republic of)	20768	58432	231853	177496	2353.0	0.980417
174	Viet Nam	20382	57104	226402	173462	2895.0	1.206250
175	Yemen	21912	58432	231568	177496	1991.0	0.829583
176	Zambia	22590	59760	236854	181530	2234.0	0.930833
177	Zimbabwe	16290	59760	236949	181530	1860.0	0.775000

178 rows × 7 columns

fig = px.bar(Foodsupply_sum, x='Area', y='SupplyPerCapita', color='SupplyPerCapita',
             #color_continuous_scale=px.colors.sequential.Viridis,
            labels={
                     "SupplyPerCapita": "Capacité<br>d'approvisionnement<br>per capita",
                     "Area": "Pays",
                 })
#fig.update_traces(marker_coloraxis=None)
fig.update_coloraxes(colorscale='Viridis')
fig.update_layout(xaxis={'categoryorder':'total descending'})
fig.update_layout(xaxis={"dtick":1},margin={"t":0,"b":0},height=600)
fig.update_xaxes(tickfont=dict(size=9))
fig.show()

fig = px.bar(Foodsupply_sum.loc[(Foodsupply_sum['SupplyPerCapita'] <0.90) | (Foodsupply_sum['SupplyPerCapita'] >1.45 )], y='Area', x='SupplyPerCapita', color='SupplyPerCapita',
             range_color=[0.75,1.60],
             #color_continuous_scale=px.colors.sequential.Viridis,
            labels={
                     "SupplyPerCapita": "Capacité<br>d'approvisionnement<br>per capita",
                     "Area": "Pays",
                 })
#fig.update_traces(marker_coloraxis=None)
fig.update_coloraxes(colorscale='Viridis')
fig.update_layout(yaxis={'categoryorder':'total ascending'})
fig.update_layout(xaxis={"dtick":1},margin={"t":0,"b":0},
                  height=1000,
                  width=800)
fig.update_xaxes(tickfont=dict(size=9))
fig.show()

# Calcule du nombre pouvant etre nourries :
# Jointure entre Foodsupply_sum et population_en['Value'].sum()*1000

Foodsupply_sum_Pop=pd.merge(Foodsupply_sum,population_en[['Value','Area']], on='Area')

Foodsupply_sum_Pop

	Area	AreaCode	ElementCode	ItemCode	Year	Value_x	SupplyPerCapita	Value_y
0	Afghanistan	152	50464	199095	153292	2303.0	0.959583	36296
1	Albania	234	51792	206162	157326	3324.0	1.385000	2884
2	Algeria	336	55776	221444	169428	3380.0	1.408333	41389
3	Angola	602	57104	226503	173462	2436.0	1.015000	29817
4	Antigua and Barbuda	696	57768	229025	175479	2444.0	1.018333	95
...	...	...	...	...	...	...	...	...
172	Venezuela (Bolivarian Republic of)	20768	58432	231853	177496	2353.0	0.980417	29402
173	Viet Nam	20382	57104	226402	173462	2895.0	1.206250	94601
174	Yemen	21912	58432	231568	177496	1991.0	0.829583	27835
175	Zambia	22590	59760	236854	181530	2234.0	0.930833	16854
176	Zimbabwe	16290	59760	236949	181530	1860.0	0.775000	14237

177 rows × 8 columns

# Multiplication de la colonne SupplyPerCapita par la population du pays

Foodsupply_sum_Pop['SupplyPop']=Foodsupply_sum_Pop['SupplyPerCapita']*Foodsupply_sum_Pop['Value_y']*1000
Foodsupply_sum_Pop

	Area	AreaCode	ElementCode	ItemCode	Year	Value_x	SupplyPerCapita	Value_y	SupplyPop
0	Afghanistan	152	50464	199095	153292	2303.0	0.959583	36296	3.482904e+07
1	Albania	234	51792	206162	157326	3324.0	1.385000	2884	3.994340e+06
2	Algeria	336	55776	221444	169428	3380.0	1.408333	41389	5.828951e+07
3	Angola	602	57104	226503	173462	2436.0	1.015000	29817	3.026425e+07
4	Antigua and Barbuda	696	57768	229025	175479	2444.0	1.018333	95	9.674167e+04
...	...	...	...	...	...	...	...	...	...
172	Venezuela (Bolivarian Republic of)	20768	58432	231853	177496	2353.0	0.980417	29402	2.882621e+07
173	Viet Nam	20382	57104	226402	173462	2895.0	1.206250	94601	1.141125e+08
174	Yemen	21912	58432	231568	177496	1991.0	0.829583	27835	2.309145e+07
175	Zambia	22590	59760	236854	181530	2234.0	0.930833	16854	1.568826e+07
176	Zimbabwe	16290	59760	236949	181530	1860.0	0.775000	14237	1.103368e+07

177 rows × 9 columns

#Somme totale
round(Foodsupply_sum_Pop['SupplyPop'].sum())

9144796081

2) le nombre théorique de personnes qui pourraient être nourries en 2017 avec des produits d’origine végétale

FoodSupply_Vege_filter = dispo_alimentaire_vegetal_en.loc[(dispo_alimentaire_vegetal_en['ElementCode'] == 664)].dropna(subset = ['Value'])
Foodsupply_Vege_sum=FoodSupply_Vege_filter.groupby(['Area'], as_index=False).sum()
Foodsupply_Vege_sum['SupplyPerCapita']=Foodsupply_Vege_sum['Value']/2400
Foodsupply_Vege_sum_Pop=pd.merge(Foodsupply_Vege_sum,population_en[['Value','Area']], on='Area')
Foodsupply_Vege_sum_Pop['SupplyPop']=Foodsupply_Vege_sum_Pop['SupplyPerCapita']*Foodsupply_Vege_sum_Pop['Value_y']*1000
round(Foodsupply_Vege_sum_Pop['SupplyPop'].sum())

7517686381

Le nombre théorique de personnes qui pourraient être nourries en 2017 avec des produits d’origine végétale est de 7517686381.

IV) Analyses des certaines causes de la sous-nutrition mondiale

1) Utilisation de la disponibilité intérieure

Nous avons vu que la production alimentaire mondiale était suffisante pour nourir la planète. La question suivante est de savoir comment est utilisé la disponibilité alimentaire des différents pays ?

Pertes + Semences + Traitement (raffinage) + Alimentation animale + Alimentation humaine = disponibilité alimentaire = Production + l’Import + l’export + Variation de stock

La première étape est de récupérer uniquement les données d’utilisation alimentaire (Pertes + Semences + Traitement (raffinage) + Alimentation animale + Alimentation humaine)

1a) Utilisation par élément et par pays

Elementcode_list = [5142,5521,5123,5527,5131,5154]

Utilisation_dispo = dispo_alimentaire_total[dispo_alimentaire_total['ElementCode'].isin(Elementcode_list)]

#Traduction des dispos en français pour la représentation graphique:
Utilisation_dispo["Element"].replace({"Feed": "Aliments.pour.animaux", "Food": "Nourriture", 'Losses' : 'Pertes','Processing' : 'Traitement','Other uses (non-food)' : 'Autres.Utilisations','Seed' : 'Semences' }, inplace=True)

B:\anaconda3\lib\site-packages\pandas\core\generic.py:6619: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

Utilisation_dispo

	Domain	AreaCode	Area	ElementCode	Element	ItemCode	Item	Year	Unit	Value
5	Vegetal	2	Afghanistan	5521	Aliments.pour.animaux	2511	Wheat and products	2017	1000 tonnes	221.00
6	Vegetal	2	Afghanistan	5527	Semences	2511	Wheat and products	2017	1000 tonnes	344.00
7	Vegetal	2	Afghanistan	5123	Pertes	2511	Wheat and products	2017	1000 tonnes	642.00
9	Vegetal	2	Afghanistan	5142	Nourriture	2511	Wheat and products	2017	1000 tonnes	5944.00
19	Vegetal	2	Afghanistan	5521	Aliments.pour.animaux	2805	Rice (Milled Equivalent)	2017	1000 tonnes	4.00
...	...	...	...	...	...	...	...	...	...	...
41764	Animal	181	Zimbabwe	5142	Nourriture	2767	Molluscs, Other	2017	1000 tonnes	0.03
41771	Animal	181	Zimbabwe	5154	Autres.Utilisations	2769	Aquatic Animals, Others	2017	1000 tonnes	0.00
41772	Animal	181	Zimbabwe	5142	Nourriture	2769	Aquatic Animals, Others	2017	1000 tonnes	0.00
41779	Animal	181	Zimbabwe	5154	Autres.Utilisations	2775	Aquatic Plants	2017	1000 tonnes	0.00
41780	Animal	181	Zimbabwe	5142	Nourriture	2775	Aquatic Plants	2017	1000 tonnes	0.00

36305 rows × 10 columns

# Calcul de la somme par Element et par Pays :
Utilisation_dispo_sum=Utilisation_dispo.groupby(['Area','Element'], as_index=False).sum()

Utilisation_dispo_sum

	Area	Element	AreaCode	ElementCode	ItemCode	Year	Value
0	Afghanistan	Aliments.pour.animaux	50	138025	64093	50425	455.00
1	Afghanistan	Autres.Utilisations	10	25770	13013	10085	40.00
2	Afghanistan	Nourriture	156	401076	204212	157326	14274.00
3	Afghanistan	Pertes	54	138321	70564	54459	1197.00
4	Afghanistan	Semences	24	66324	30931	24204	424.00
...	...	...	...	...	...	...	...
1063	Zimbabwe	Autres.Utilisations	5430	154620	79731	60510	16.06
1064	Zimbabwe	Nourriture	16290	462780	236949	181530	4307.20
1065	Zimbabwe	Pertes	7421	210043	106573	82697	375.00
1066	Zimbabwe	Semences	2534	77378	35975	28238	67.00
1067	Zimbabwe	Traitement	6335	179585	90779	70595	3460.00

1068 rows × 7 columns

# Calcul de la proportion de la valeur des Elements par Pays :
Utilisation_dispo_sum['Proportion']=(Utilisation_dispo_sum['Value'])/Utilisation_dispo_sum.groupby('Area')['Value'].transform('sum')
Utilisation_dispo_sum

	Area	Element	AreaCode	ElementCode	ItemCode	Year	Value	Proportion
0	Afghanistan	Aliments.pour.animaux	50	138025	64093	50425	455.00	0.026429
1	Afghanistan	Autres.Utilisations	10	25770	13013	10085	40.00	0.002323
2	Afghanistan	Nourriture	156	401076	204212	157326	14274.00	0.829112
3	Afghanistan	Pertes	54	138321	70564	54459	1197.00	0.069528
4	Afghanistan	Semences	24	66324	30931	24204	424.00	0.024628
...	...	...	...	...	...	...	...	...
1063	Zimbabwe	Autres.Utilisations	5430	154620	79731	60510	16.06	0.001890
1064	Zimbabwe	Nourriture	16290	462780	236949	181530	4307.20	0.506853
1065	Zimbabwe	Pertes	7421	210043	106573	82697	375.00	0.044128
1066	Zimbabwe	Semences	2534	77378	35975	28238	67.00	0.007884
1067	Zimbabwe	Traitement	6335	179585	90779	70595	3460.00	0.407158

1068 rows × 8 columns

(ggplot(Utilisation_dispo_sum, aes(x='Area', y='Proportion', fill='Element'))
 + geom_col()
 + theme(figure_size=(30, 10))
 + theme(axis_text_x = element_text(angle = 45, hjust = 1))
 #+ scale_fill_brewer(type="qual",palette="Accent")
)

<ggplot: (90654168826)>

1b) Utilisation Globale par élément

#Somme des Elements
Utilisation_dispo_sumTot=Utilisation_dispo.groupby(['Element'], as_index=False).sum()
Utilisation_dispo_sumTot

	Element	AreaCode	ElementCode	ItemCode	Year	Value
0	Aliments.pour.animaux	544994	23530502	11034190	8596454	1357634.03
1	Autres.Utilisations	693089	28269690	14608994	11063245	849708.08
2	Nourriture	1934434	78518340	40238987	30799590	5265476.89
3	Pertes	758233	30538203	15535220	12023337	613028.00
4	Semences	268412	11700659	5441303	4269989	169176.05
5	Traitement	399341	16470510	8323534	6474570	2824007.00

# Calcul des proportions par élément
Utilisation_dispo_sumTot['Proportion']=(Utilisation_dispo_sumTot['Value'])/Utilisation_dispo_sumTot['Value'].sum()
Utilisation_dispo_sumTot

	Element	AreaCode	ElementCode	ItemCode	Year	Value	Proportion
0	Aliments.pour.animaux	544994	23530502	11034190	8596454	1357634.03	0.122541
1	Autres.Utilisations	693089	28269690	14608994	11063245	849708.08	0.076695
2	Nourriture	1934434	78518340	40238987	30799590	5265476.89	0.475265
3	Pertes	758233	30538203	15535220	12023337	613028.00	0.055332
4	Semences	268412	11700659	5441303	4269989	169176.05	0.015270
5	Traitement	399341	16470510	8323534	6474570	2824007.00	0.254897

#define data
#data = [value1, value2, value3, ...]
#labels = ['label1', 'label2', 'label3', ...]

#define Seaborn color palette to use
colors = sns.color_palette('hls')[0:6]

#fig_dims = (6, 6)
#fig, ax = plt.subplots(figsize=fig_dims)
plt.rcParams["figure.figsize"] = (8,10)
plt.pie(Utilisation_dispo_sumTot['Proportion'], labels = Utilisation_dispo_sumTot["Element"], colors = colors, autopct='%.0f%%')
plt.show()

2a ) Utilisation des céréales par élément et par pays

Utilisation_dispo_cereal = bilan_cereale[bilan_cereale['Element Code'].isin(Elementcode_list)]
Utilisation_dispo_cereal_sum=Utilisation_dispo_cereal.groupby(['Area','Element'], as_index=False).sum()
Utilisation_dispo_cereal_sum['Proportion']=(Utilisation_dispo_cereal_sum['Value'])/Utilisation_dispo_cereal_sum.groupby('Area')['Value'].transform('sum')

(ggplot(Utilisation_dispo_cereal_sum, aes(x='Area', y='Proportion', fill='Element'))
 + geom_col()
 + theme(figure_size=(30, 10))
 + theme(axis_text_x = element_text(angle = 45, hjust = 1))
 #+ scale_fill_brewer(type="qual",palette="Accent")
)

<ggplot: (90654622480)>

2b) Utilisation des céréales par élément

#bilan_cereale
Utilisation_dispo_cereal_sumTot=Utilisation_dispo_cereal.groupby(['Element'], as_index=False).sum()
Utilisation_dispo_cereal_sumTot['Proportion']=(Utilisation_dispo_cereal_sumTot['Value'])/Utilisation_dispo_cereal_sumTot['Value'].sum()
Utilisation_dispo_cereal_sumTot

	Element	Area Code	Element Code	Year	Value	Proportion
0	Aliments.pour.animaux	22581	982738	359026	935717	0.328096
1	Autres.Utilisations	21057	865872	338856	279627	0.098047
2	Nourriture	22581	915276	359026	1310653	0.459562
3	Pertes	21938	891402	350958	135469	0.047500
4	Semences	21341	928536	338856	80196	0.028120
5	Traitement	21181	867139	340873	110302	0.038676

#create pie chart
plt.pie(Utilisation_dispo_cereal_sumTot['Proportion'], labels = Utilisation_dispo_cereal_sumTot["Element"], colors = colors, autopct='%.0f%%')
plt.rcParams["figure.figsize"] = (8,10)

plt.show()

# Calcul de la proportion de la valeur des Elements par Pays :
Utilisation_dispo_sum['Proportion']=(Utilisation_dispo_sum['Value'])/Utilisation_dispo_sum.groupby('Area')['Value'].transform('sum')
Utilisation_dispo_sum

	Area	Element	AreaCode	ElementCode	ItemCode	Year	Value	Proportion
0	Afghanistan	Aliments.pour.animaux	50	138025	64093	50425	455.00	0.026429
1	Afghanistan	Autres.Utilisations	10	25770	13013	10085	40.00	0.002323
2	Afghanistan	Nourriture	156	401076	204212	157326	14274.00	0.829112
3	Afghanistan	Pertes	54	138321	70564	54459	1197.00	0.069528
4	Afghanistan	Semences	24	66324	30931	24204	424.00	0.024628
...	...	...	...	...	...	...	...	...
1063	Zimbabwe	Autres.Utilisations	5430	154620	79731	60510	16.06	0.001890
1064	Zimbabwe	Nourriture	16290	462780	236949	181530	4307.20	0.506853
1065	Zimbabwe	Pertes	7421	210043	106573	82697	375.00	0.044128
1066	Zimbabwe	Semences	2534	77378	35975	28238	67.00	0.007884
1067	Zimbabwe	Traitement	6335	179585	90779	70595	3460.00	0.407158

1068 rows × 8 columns

3) Utilisation par PIB

UtilisationcerealPIB=pd.merge(Utilisation_dispo_cereal_sum,(pib_en.loc[(pib_en['ElementCode'] == 6119)]), on='Area', how='left')
UtilisationcerealPIB

	Area	Element_x	Area Code	Element Code	Year_x	Value_x	Proportion	AreaCode	ElementCode	Element_y	Item Code	Item	Year_y	Unit	Value_y
0	Afghanistan	Aliments.pour.animaux	2	5521	2017	403	0.048854	2.0	6119.0	Value US$ per capita	22008.0	Gross Domestic Product	2017.0	US$	513.085978
1	Afghanistan	Autres.Utilisations	2	5154	2017	7	0.000849	2.0	6119.0	Value US$ per capita	22008.0	Gross Domestic Product	2017.0	US$	513.085978
2	Afghanistan	Nourriture	2	5142	2017	6746	0.817796	2.0	6119.0	Value US$ per capita	22008.0	Gross Domestic Product	2017.0	US$	513.085978
3	Afghanistan	Pertes	2	5123	2017	706	0.085586	2.0	6119.0	Value US$ per capita	22008.0	Gross Domestic Product	2017.0	US$	513.085978
4	Afghanistan	Semences	2	5527	2017	387	0.046915	2.0	6119.0	Value US$ per capita	22008.0	Gross Domestic Product	2017.0	US$	513.085978
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1030	Zimbabwe	Aliments.pour.animaux	181	5521	2017	226	0.106755	181.0	6119.0	Value US$ per capita	22008.0	Gross Domestic Product	2017.0	US$	1548.186368
1031	Zimbabwe	Nourriture	181	5142	2017	1695	0.800661	181.0	6119.0	Value US$ per capita	22008.0	Gross Domestic Product	2017.0	US$	1548.186368
1032	Zimbabwe	Pertes	181	5123	2017	80	0.037789	181.0	6119.0	Value US$ per capita	22008.0	Gross Domestic Product	2017.0	US$	1548.186368
1033	Zimbabwe	Semences	181	5527	2017	44	0.020784	181.0	6119.0	Value US$ per capita	22008.0	Gross Domestic Product	2017.0	US$	1548.186368
1034	Zimbabwe	Traitement	181	5131	2017	72	0.034010	181.0	6119.0	Value US$ per capita	22008.0	Gross Domestic Product	2017.0	US$	1548.186368

1035 rows × 15 columns

fig = px.scatter(UtilisationcerealPIB, x="Value_y", y="Proportion",facet_col="Element_x",facet_col_wrap=3,
                  trendline="ols",
            labels={
                     "Value_y": "PIB per capita du pays",
                     "Element_x": "Utilisation",
                     "Proportion": "Proportion",
                 },
                 height=600,
                  width=1000)
fig.show()

import statsmodels.api
import statsmodels.formula.api
model = statsmodels.formula.api.ols('Proportion ~ Value_y', data = UtilisationcerealPIB.loc[(UtilisationcerealPIB['Element_x'] == "Aliments.pour.animaux")])
result = model.fit()

result.summary()

OLS Regression Results

Dep. Variable:	Proportion	R-squared:	0.250
Model:	OLS	Adj. R-squared:	0.246
Method:	Least Squares	F-statistic:	58.40
Date:	Tue, 15 Feb 2022	Prob (F-statistic):	1.35e-12
Time:	15:58:48	Log-Likelihood:	44.223
No. Observations:	177	AIC:	-84.45
Df Residuals:	175	BIC:	-78.09
Df Model:	1
Covariance Type:	nonrobust

	coef	std err	t	P>|t|	[0.025	0.975]
Intercept	0.2058	0.017	11.790	0.000	0.171	0.240
Value_y	5.738e-06	7.51e-07	7.642	0.000	4.26e-06	7.22e-06

Omnibus:	0.113	Durbin-Watson:	1.938
Prob(Omnibus):	0.945	Jarque-Bera (JB):	0.242
Skew:	-0.041	Prob(JB):	0.886
Kurtosis:	2.839	Cond. No.	2.85e+04

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 2.85e+04. This might indicate that there are
strong multicollinearity or other numerical problems.

print(result.f_pvalue) 
print('_____________________')
print(result.pvalues)

1.3500139033487798e-12
_____________________
Intercept    5.483377e-24
Value_y      1.350014e-12
dtype: float64

La part de l'utilisation des céréales pour les animaux est plus important dans les pays avec un plus fort PIB par habitant.

La disponibilité intérieure total est en majeur partie utilisé pour l’alimentation humaine, mais certaines catégories ( céréales ) sont aussi massivement utilisé pour les animaux.

La répartition de l’utilisation de la dispo intérieure dépend des pays ( céréales et totale )

Les pays à fort PIB ont une plus forte utilisation des céréales pour les animaux.

2) L'inégalité de l'accès à la nourriture

ssNutriPrev_PIBperC=pd.merge(ssNutri_Prev_filter,(pib_en.loc[(pib_en['ElementCode'] == 6119)]), on='Area')
ssNutriPrev_PIBperC

	AreaCode_x	Area	ElementCode_x	Element_x	ItemCode	Item_x	Year_x	Unit_x	Value_x	Note	AreaCode_y	ElementCode_y	Element_y	Item Code	Item_y	Year_y	Unit_y	Value_y
0	2	Afghanistan	6121	Value	210041	Prevalence of undernourishment (percent) (3-ye...	2016-2018	%	23.0	NaN	2	6119	Value US$ per capita	22008	Gross Domestic Product	2017	US$	513.085978
1	3	Albania	6121	Value	210041	Prevalence of undernourishment (percent) (3-ye...	2016-2018	%	4.1	NaN	3	6119	Value US$ per capita	22008	Gross Domestic Product	2017	US$	4514.204908
2	4	Algeria	6121	Value	210041	Prevalence of undernourishment (percent) (3-ye...	2016-2018	%	2.7	NaN	4	6119	Value US$ per capita	22008	Gross Domestic Product	2017	US$	4109.701336
3	7	Angola	6121	Value	210041	Prevalence of undernourishment (percent) (3-ye...	2016-2018	%	15.4	NaN	7	6119	Value US$ per capita	22008	Gross Domestic Product	2017	US$	4095.811686
4	9	Argentina	6121	Value	210041	Prevalence of undernourishment (percent) (3-ye...	2016-2018	%	3.1	NaN	9	6119	Value US$ per capita	22008	Gross Domestic Product	2017	US$	14627.615635
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
104	215	United Republic of Tanzania	6121	Value	210041	Prevalence of undernourishment (percent) (3-ye...	2016-2018	%	24.1	NaN	215	6119	Value US$ per capita	22008	Gross Domestic Product	2017	US$	1001.160919
105	155	Vanuatu	6121	Value	210041	Prevalence of undernourishment (percent) (3-ye...	2016-2018	%	9.6	NaN	155	6119	Value US$ per capita	22008	Gross Domestic Product	2017	US$	3082.368364
106	236	Venezuela (Bolivarian Republic of)	6121	Value	210041	Prevalence of undernourishment (percent) (3-ye...	2016-2018	%	22.2	NaN	236	6119	Value US$ per capita	22008	Gross Domestic Product	2017	US$	8429.074575
107	237	Viet Nam	6121	Value	210041	Prevalence of undernourishment (percent) (3-ye...	2016-2018	%	7.2	NaN	237	6119	Value US$ per capita	22008	Gross Domestic Product	2017	US$	2365.521494
108	249	Yemen	6121	Value	210041	Prevalence of undernourishment (percent) (3-ye...	2016-2018	%	46.6	NaN	249	6119	Value US$ per capita	22008	Gross Domestic Product	2017	US$	745.087909

109 rows × 18 columns

fig = px.scatter(ssNutriPrev_PIBperC, x="Value_y", y="Value_x",color="Area",
            labels={
                     "Value_y": "PIB per capita du pays",
                     "Value_x": "Pourcentage de personne<br>en état de sous-nutrition",
                     "Area": "Pays",
                 })
fig.show()

ssNutriTot_PIBperC=pd.merge(ssNutri_filter,(pib_en.loc[(pib_en['ElementCode'] == 6119)]), on='Area')
ssNutriTot_PIBperC

	AreaCode_x	Area	ElementCode_x	Element_x	ItemCode	Item_x	Year_x	Unit_x	Value_x	Note	AreaCode_y	ElementCode_y	Element_y	Item Code	Item_y	Year_y	Unit_y	Value_y
0	2	Afghanistan	6132	Value	210011	Number of people undernourished (million) (3-y...	2016-2018	millions	8.3	NaN	2	6119	Value US$ per capita	22008	Gross Domestic Product	2017	US$	513.085978
1	3	Albania	6132	Value	210011	Number of people undernourished (million) (3-y...	2016-2018	millions	0.1	NaN	3	6119	Value US$ per capita	22008	Gross Domestic Product	2017	US$	4514.204908
2	4	Algeria	6132	Value	210011	Number of people undernourished (million) (3-y...	2016-2018	millions	1.1	NaN	4	6119	Value US$ per capita	22008	Gross Domestic Product	2017	US$	4109.701336
3	7	Angola	6132	Value	210011	Number of people undernourished (million) (3-y...	2016-2018	millions	4.6	NaN	7	6119	Value US$ per capita	22008	Gross Domestic Product	2017	US$	4095.811686
4	9	Argentina	6132	Value	210011	Number of people undernourished (million) (3-y...	2016-2018	millions	1.4	NaN	9	6119	Value US$ per capita	22008	Gross Domestic Product	2017	US$	14627.615635
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
85	225	United Arab Emirates	6132	Value	210011	Number of people undernourished (million) (3-y...	2016-2018	millions	0.3	NaN	225	6119	Value US$ per capita	22008	Gross Domestic Product	2017	US$	40644.804043
86	215	United Republic of Tanzania	6132	Value	210011	Number of people undernourished (million) (3-y...	2016-2018	millions	13.2	NaN	215	6119	Value US$ per capita	22008	Gross Domestic Product	2017	US$	1001.160919
87	236	Venezuela (Bolivarian Republic of)	6132	Value	210011	Number of people undernourished (million) (3-y...	2016-2018	millions	6.5	NaN	236	6119	Value US$ per capita	22008	Gross Domestic Product	2017	US$	8429.074575
88	237	Viet Nam	6132	Value	210011	Number of people undernourished (million) (3-y...	2016-2018	millions	6.8	NaN	237	6119	Value US$ per capita	22008	Gross Domestic Product	2017	US$	2365.521494
89	249	Yemen	6132	Value	210011	Number of people undernourished (million) (3-y...	2016-2018	millions	13.0	NaN	249	6119	Value US$ per capita	22008	Gross Domestic Product	2017	US$	745.087909

90 rows × 18 columns

fig = px.scatter(ssNutriTot_PIBperC, x="Value_y", y="Value_x",color="Area",
            labels={
                     "Value_y": "PIB per capita du pays",
                     "Value_x": "Nombre total de personne<br>en état de sous-nutrition",
                     "Area": "Pays",
                 })
fig.show()

Certains Pays avec un fort PIB par habitant ont une part conséquente de personnes en état de sous nutrition :

• Nouvelle Calédonie
• Emirats Arabes Unis
• Macao

Le cas de l'Inde :

• Capacité d'approvisionnement de 1.06 (elle est capable de produire 106% des besoins journaliers de ses habitants)
• 13.8% de la population sous alimentée
• Soit 185 Millions de personne

3) La politique d'utilisation de la disponibilité : Exemple ed la Thaïlande et du manioc

a) Rappel des chiffres de la population totale et sous-alimentée

# La population totale de la Thailand
(population_en['Value'].loc[(population_en['Area'] == "Thailand")]*1000).values[0]

69210000

# Nombre de personne en sous nutrition
PopThaiSSnutri=(pd.to_numeric(sous_nutrition_en['Value'].loc[(sous_nutrition_en['Area'] == "Thailand") & (sous_nutrition_en['ItemCode'] == 210011)])*1000000).values[0]
PopThaiSSnutri

5400000.0

# Pourcentage de la population en état de sous nutrition
pd.to_numeric(sous_nutrition_en['Value'].loc[(sous_nutrition_en['Area'] == "Thailand") & (sous_nutrition_en['ItemCode'] == 210041)]).values[0]

7.7

La population totale de la Thailand est de 69210000 habitants dont 5400000 personnes sont en état de sous nutrition, soit 7.7 % de la population.

b) L’utilisation de la disponibilité du manioc

# Valeur de la production de manioc
productionManiocThai=pd.to_numeric(dispo_alimentaire_total['Value'].loc[(dispo_alimentaire_total['Area'] == "Thailand") & 
                                                (dispo_alimentaire_total['Item'] == 'Cassava and products') & 
                                                (dispo_alimentaire_total['Element'] == "Production")]).values[0]
productionManiocThai

30495.0

# Valeur de l'exportation de manioc
exportationManiocThai=pd.to_numeric(dispo_alimentaire_total['Value'].loc[(dispo_alimentaire_total['Area'] == "Thailand") & 
                                                (dispo_alimentaire_total['Item'] == 'Cassava and products') & 
                                                (dispo_alimentaire_total['Element'] == "Export Quantity")]).values[0]
exportationManiocThai

29409.0

round((exportationManiocThai/productionManiocThai)*100,2)

96.44

La Thailand produit 30495 tonnes de manioc et ses dérivés. Cependant, elle en exporte 29409 tonnes soit 96.44 % de sa production.

c) Le manioc pour combattre la sous-nutrition

capaciteNutritionManioc=((157*productionManiocThai*1000)/0.1)/2400
print(capaciteNutritionManioc)

19948812.5

maniocRequis=(PopThaiSSnutri*productionManiocThai)/capaciteNutritionManioc
print(maniocRequis)

8254.777070063694

pourcentageProduction=(maniocRequis/productionManiocThai)*100
round(pourcentageProduction,2)

27.07

La valeur energétique du manioc est de 157 kcal pour 100g (0,1 kg). Avec une production de 30495 tonnes, la Thailand serait en capacité de nourrir 19948812 personnes. Afin de subvenir au besoin de sa population en sous-nutrition, il faudrait 8254.78 tonnes de manioc, soit 27.07 % de leur production totale.